Will they participate? Predicting patients' response to clinical trial invitations in a pediatric emergency department. (27th April 2016)

• Record Type: Journal Article
• Authors: Ni, Yizhao; Beck, Andrew F; Taylor, Regina; Dyas, Jenna; Solti, Imre; Grupp-Phelan, Jacqueline; Dexheimer, Judith W
• Is Part Of: Journal of the American Medical Informatics Association. Volume 23:Number 4(2016:Jul.)
• Page Start: 671
• Page End: 680
• Publication Date: 2016-04-27
• Subjects: patient-directed precision recruitment -- socioeconomic status -- predictive modeling -- machine learning; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv216
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective (1) To develop an automated algorithm to predict a patient's response (ie, if the patient agrees or declines) before he/she is approached for a clinical trial invitation; (2) to assess the algorithm performance and the predictors on real-world patient recruitment data for a diverse set of clinical trials in a pediatric emergency department; and (3) to identify directions for future studies in predicting patients' participation response. Materials and Methods We collected 3345 patients' response to trial invitations on 18 clinical trials at one center that were actively enrolling patients between January 1, 2010 and December 31, 2012. In parallel, we retrospectively extracted demographic, socioeconomic, and clinical predictors from multiple sources to represent the patients' profiles. Leveraging machine learning methodology, the automated algorithms predicted participation response for individual patients and identified influential features associated with their decision-making. The performance was validated on the collection of actual patient response, where precision, recall, F -measure, and area under the ROC curve were assessed. Results Compared to the random response predictor that simulated the current practice, the machine learning algorithms achieved significantly better performance (Precision/Recall/ F -measure/area under the ROC curve: 70.82%/92.02%/80.04%/72.78% on 10-fold cross validation and 71.52%/92.68%/80.74%/75.74% on the test set). By analyzing the significant features output by the algorithms, the study confirmed several literature findings and identified challenges that could be mitigated to optimize recruitment. Conclusion By exploiting predictive variables from multiple sources, we demonstrated that machine learning algorithms have great potential in improving the effectiveness of the recruitment process by automatically predicting patients' participation response to trial invitations.